# α-Mutual Information: A Tunable Privacy Measure for Privacy Protection in Data Sharing

arXiv: https://arxiv.org/abs/2310.18241 · HTML: https://mlsanity.com/paper/2310.18241/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 27 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective](https://mlsanity.com/paper/2107.01475/) (2021)
- [Privacy-Preserving Adversarial Networks](https://mlsanity.com/paper/1712.07008/) (2017)
- [Generating Fair Universal Representations using Adversarial Models](https://mlsanity.com/paper/1910.00411/) (2019)
- [Real-Time Privacy-Preserving Data Release for Smart Meters](https://mlsanity.com/paper/1906.06427/) (2019)
- [Deep Directed Information-Based Learning for Privacy-Preserving Smart Meter Data Release](https://mlsanity.com/paper/2011.11421/) (2020)
- [On the Impact of Side Information on Smart Meter Privacy-Preserving Methods](https://mlsanity.com/paper/2006.16062/) (2020)

## Abstract (arXiv)

This paper adopts Arimoto's α-Mutual Information as a tunable privacy measure, in a privacy-preserving data release setting that aims to prevent disclosing private data to adversaries. By fine-tuning the privacy metric, we demonstrate that our approach yields superior models that effectively thwart attackers across various performance dimensions. We formulate a general distortion-based mechanism that manipulates the original data to offer privacy protection. The distortion metrics are determined according to the data structure of a specific experiment. We confront the problem expressed in the formulation by employing a general adversarial deep learning framework that consists of a releaser and an adversary, trained with opposite goals. This study conducts empirical experiments on images and time-series data to verify the functionality of α-Mutual Information. We evaluate the privacy-utility trade-off of customized models and compare them to mutual information as the baseline measure. Finally, we analyze the consequence of an attacker's access to side information about private data and witness that adapting the privacy measure results in a more refined model than the state-of-the-art in terms of resiliency against side information.